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What Determines Thalamic Spatio-Temporal Properties?

什么决定丘脑时空特性？

基本信息

批准号：
7896509
负责人：
EHUD KAPLAN
金额：
$ 40.73万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-01 至 2012-08-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): How do thalamic neurons integrate their various feedforward and feedback inputs? Most of the inputs and synapses in the mammalian lateral geniculate nucleus (LGN) are extra-retinal, but the way in which these diverse inputs are integrated to control the flow of visual information from retina to cortex is not understood. In particular, the influence of the descending inputs from the cortex and the perigeniculate nucleus (PGN) on the spatiotemporal properties of receptive fields of LGN relay neurons is unknown, although the size and complexity of these inputs strongly suggest their importance, without them, the LGN might arguably be unnecessary. To address this knowledge gap, we shall combine physiological experiments with computational modeling to achieve the following aims: 1) Measure the spatiotemporal structure of receptive fields in the cat LGN before and during (reversible) inactivation of the descending feedback pathway from V1; 2) Compare spatial summation in the retina with that of LGN neurons with and without V1 feedback; 3) Measure the temporal transfer function of neurons in layer 6 of V1; 4) Construct computational models of LGN relay neurons that incorporate the descending pathway from V1 and the PGN, and 5) Validate the models' predictions against physiological measurements. The proposed modeling, which builds on our initial (static) feedback model, will employ the innovative simulation approach of population kinetics, and will be one of the first attempts to model the corticothalamic feedback and its dynamics. It will use parallel computation on a scale not commonly found in neuroscience: two clusters of powerful computers, and a very large IBM supercomputer, which can accommodate larger, more complex models than could have been attempted in the past. The results will advance our understanding of the role that the descending inputs to the LGN play in establishing its sensitivity, dynamics, receptive field structure and discharge pattern, and will provide a necessary stepping stone for future expansions of our evolving model of the early visual system. HEALTH RELEVANCE: A more complete knowledge of how the LGN combines its diverse inputs, and especially how its temporal behavior depends on the cortex, should lead to insights into its role in dynamical brain diseases, such as epilepsy. More generally, an understanding of the role of feedback circuits will help us understand other dynamical pathologies, such as Parkinson's disease.

描述（由申请人提供）：丘脑神经元如何整合其各种前馈和反馈输入？哺乳动物外侧膝状核（LGN）中的大多数输入和突触都位于视网膜外，但这些不同的输入如何整合以控制视觉信息从视网膜到皮层的流动尚不清楚。特别是，来自皮层和膝周核（PGN）的下行输入对 LGN 中继神经元感受野的时空特性的影响尚不清楚，尽管这些输入的大小和复杂性强烈表明它们的重要性，但如果没有它们，LGN 可能是不必要的。为了解决这一知识差距，我们将生理实验与计算模型结合起来，以实现以下目标：1）测量来自 V1 的下降反馈通路（可逆）失活之前和期间猫 LGN 感受野的时空结构； 2）比较有和没有V1反馈时视网膜中的空间总和与LGN神经元的空间总和； 3）测量V1第6层神经元的时间传递函数； 4) 构建 LGN 中继神经元的计算模型，其中包含 V1 和 PGN 的下行通路，以及 5) 根据生理测量结果验证模型的预测。所提出的模型建立在我们最初的（静态）反馈模型的基础上，将采用群体动力学的创新模拟方法，并且将是对皮质丘脑反馈及其动力学进行建模的首次尝试之一。它将使用神经科学中不常见的规模的并行计算：两个强大的计算机集群和一个非常大的 IBM 超级计算机，它可以容纳比过去尝试过的更大、更复杂的模型。这些结果将促进我们对 LGN 的下降输入在建立其灵敏度、动力学、感受野结构和放电模式中所起的作用的理解，并将为我们早期视觉系统进化模型的未来扩展提供必要的垫脚石。健康相关性：更全面地了解 LGN 如何结合其不同的输入，尤其是其时间行为如何依赖于皮质，应该有助于深入了解其在癫痫等动态脑疾病中的作用。更一般地说，了解反馈回路的作用将有助于我们了解其他动态病理学，例如帕金森病。